Overview

Dataset statistics

Number of variables48
Number of observations1293
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory485.0 KiB
Average record size in memory384.1 B

Variable types

Numeric17
Categorical31

Alerts

df_index is highly correlated with IdHigh correlation
Id is highly correlated with df_indexHigh correlation
LotFrontage is highly correlated with LotAreaHigh correlation
LotArea is highly correlated with LotFrontageHigh correlation
OverallQual is highly correlated with YearBuilt and 4 other fieldsHigh correlation
YearBuilt is highly correlated with OverallQual and 3 other fieldsHigh correlation
BsmtFinSF1 is highly correlated with BsmtUnfSF and 1 other fieldsHigh correlation
BsmtUnfSF is highly correlated with BsmtFinSF1 and 1 other fieldsHigh correlation
TotalBsmtSF is highly correlated with 1stFlrSF and 1 other fieldsHigh correlation
1stFlrSF is highly correlated with TotalBsmtSF and 1 other fieldsHigh correlation
GrLivArea is highly correlated with OverallQual and 4 other fieldsHigh correlation
BsmtFullBath is highly correlated with BsmtFinSF1 and 1 other fieldsHigh correlation
FullBath is highly correlated with OverallQual and 5 other fieldsHigh correlation
BedroomAbvGr is highly correlated with GrLivArea and 1 other fieldsHigh correlation
TotRmsAbvGrd is highly correlated with GrLivArea and 3 other fieldsHigh correlation
GarageCars is highly correlated with OverallQual and 4 other fieldsHigh correlation
GarageArea is highly correlated with GarageCars and 1 other fieldsHigh correlation
SalePrice is highly correlated with OverallQual and 8 other fieldsHigh correlation
df_index is highly correlated with IdHigh correlation
Id is highly correlated with df_indexHigh correlation
LotFrontage is highly correlated with LotAreaHigh correlation
LotArea is highly correlated with LotFrontageHigh correlation
OverallQual is highly correlated with YearBuilt and 5 other fieldsHigh correlation
YearBuilt is highly correlated with OverallQual and 2 other fieldsHigh correlation
BsmtFinSF1 is highly correlated with BsmtUnfSF and 1 other fieldsHigh correlation
BsmtUnfSF is highly correlated with BsmtFinSF1High correlation
TotalBsmtSF is highly correlated with 1stFlrSF and 1 other fieldsHigh correlation
1stFlrSF is highly correlated with TotalBsmtSF and 1 other fieldsHigh correlation
GrLivArea is highly correlated with OverallQual and 4 other fieldsHigh correlation
BsmtFullBath is highly correlated with BsmtFinSF1High correlation
FullBath is highly correlated with OverallQual and 4 other fieldsHigh correlation
BedroomAbvGr is highly correlated with GrLivArea and 1 other fieldsHigh correlation
TotRmsAbvGrd is highly correlated with GrLivArea and 3 other fieldsHigh correlation
GarageCars is highly correlated with OverallQual and 4 other fieldsHigh correlation
GarageArea is highly correlated with OverallQual and 2 other fieldsHigh correlation
SalePrice is highly correlated with OverallQual and 8 other fieldsHigh correlation
df_index is highly correlated with IdHigh correlation
Id is highly correlated with df_indexHigh correlation
OverallQual is highly correlated with YearBuilt and 3 other fieldsHigh correlation
YearBuilt is highly correlated with OverallQualHigh correlation
BsmtFinSF1 is highly correlated with BsmtFullBathHigh correlation
TotalBsmtSF is highly correlated with 1stFlrSFHigh correlation
1stFlrSF is highly correlated with TotalBsmtSFHigh correlation
GrLivArea is highly correlated with FullBath and 2 other fieldsHigh correlation
BsmtFullBath is highly correlated with BsmtFinSF1High correlation
FullBath is highly correlated with OverallQual and 3 other fieldsHigh correlation
BedroomAbvGr is highly correlated with TotRmsAbvGrdHigh correlation
TotRmsAbvGrd is highly correlated with GrLivArea and 1 other fieldsHigh correlation
GarageCars is highly correlated with OverallQual and 3 other fieldsHigh correlation
GarageArea is highly correlated with GarageCarsHigh correlation
SalePrice is highly correlated with OverallQual and 3 other fieldsHigh correlation
MSZoning is highly correlated with NeighborhoodHigh correlation
Neighborhood is highly correlated with MSZoning and 2 other fieldsHigh correlation
size is highly correlated with Neighborhood and 1 other fieldsHigh correlation
BldgType is highly correlated with sizeHigh correlation
GarageQual is highly correlated with GarageCondHigh correlation
GarageCond is highly correlated with GarageQualHigh correlation
BsmtQual is highly correlated with NeighborhoodHigh correlation
df_index is highly correlated with IdHigh correlation
Id is highly correlated with df_indexHigh correlation
MSSubClass is highly correlated with LotArea and 10 other fieldsHigh correlation
MSZoning is highly correlated with LotArea and 4 other fieldsHigh correlation
LotFrontage is highly correlated with LotArea and 1 other fieldsHigh correlation
LotArea is highly correlated with MSSubClass and 6 other fieldsHigh correlation
LandContour is highly correlated with NeighborhoodHigh correlation
Neighborhood is highly correlated with MSSubClass and 28 other fieldsHigh correlation
BldgType is highly correlated with MSSubClass and 4 other fieldsHigh correlation
HouseStyle is highly correlated with MSSubClass and 4 other fieldsHigh correlation
OverallQual is highly correlated with Neighborhood and 10 other fieldsHigh correlation
OverallCond is highly correlated with Neighborhood and 6 other fieldsHigh correlation
YearBuilt is highly correlated with MSSubClass and 18 other fieldsHigh correlation
Exterior1st is highly correlated with Neighborhood and 6 other fieldsHigh correlation
MasVnrType is highly correlated with Neighborhood and 3 other fieldsHigh correlation
ExterQual is highly correlated with Neighborhood and 10 other fieldsHigh correlation
ExterCond is highly correlated with OverallCondHigh correlation
Foundation is highly correlated with MSZoning and 6 other fieldsHigh correlation
BsmtQual is highly correlated with MSSubClass and 11 other fieldsHigh correlation
BsmtCond is highly correlated with OverallCondHigh correlation
BsmtFinType1 is highly correlated with Neighborhood and 6 other fieldsHigh correlation
BsmtFinSF1 is highly correlated with BsmtFinType1 and 5 other fieldsHigh correlation
BsmtFinType2 is highly correlated with BsmtFinType1High correlation
BsmtUnfSF is highly correlated with BsmtFinType1 and 6 other fieldsHigh correlation
TotalBsmtSF is highly correlated with Neighborhood and 7 other fieldsHigh correlation
HeatingQC is highly correlated with Neighborhood and 3 other fieldsHigh correlation
1stFlrSF is highly correlated with LotArea and 7 other fieldsHigh correlation
GrLivArea is highly correlated with Neighborhood and 6 other fieldsHigh correlation
BsmtFullBath is highly correlated with BsmtFinType1 and 2 other fieldsHigh correlation
FullBath is highly correlated with MSSubClass and 13 other fieldsHigh correlation
HalfBath is highly correlated with MSSubClass and 1 other fieldsHigh correlation
BedroomAbvGr is highly correlated with MSSubClass and 3 other fieldsHigh correlation
KitchenQual is highly correlated with Neighborhood and 7 other fieldsHigh correlation
TotRmsAbvGrd is highly correlated with GrLivArea and 3 other fieldsHigh correlation
Fireplaces is highly correlated with NeighborhoodHigh correlation
GarageType is highly correlated with Neighborhood and 1 other fieldsHigh correlation
GarageFinish is highly correlated with Neighborhood and 6 other fieldsHigh correlation
GarageCars is highly correlated with MSSubClass and 10 other fieldsHigh correlation
GarageArea is highly correlated with Neighborhood and 6 other fieldsHigh correlation
GarageQual is highly correlated with Foundation and 1 other fieldsHigh correlation
GarageCond is highly correlated with GarageQualHigh correlation
PavedDrive is highly correlated with NeighborhoodHigh correlation
SalePrice is highly correlated with MSZoning and 18 other fieldsHigh correlation
size is highly correlated with MSSubClass and 4 other fieldsHigh correlation
df_index is uniformly distributed Uniform
Id is uniformly distributed Uniform
df_index has unique values Unique
Id has unique values Unique
BsmtFinSF1 has 386 (29.9%) zeros Zeros
BsmtUnfSF has 72 (5.6%) zeros Zeros

Reproduction

Analysis started2022-06-07 12:39:29.262253
Analysis finished2022-06-07 12:40:40.664102
Duration1 minute and 11.4 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1293
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean733.2552204
Minimum0
Maximum1459
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:41.651884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile69.6
Q1367
median737
Q31099
95-th percentile1388.4
Maximum1459
Range1459
Interquartile range (IQR)732

Descriptive statistics

Standard deviation422.3313701
Coefficient of variation (CV)0.5759677645
Kurtosis-1.193977765
Mean733.2552204
Median Absolute Deviation (MAD)366
Skewness-0.01524407151
Sum948099
Variance178363.7862
MonotonicityStrictly increasing
2022-06-07T09:40:41.843884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
10081
 
0.1%
9851
 
0.1%
9831
 
0.1%
9821
 
0.1%
9811
 
0.1%
9801
 
0.1%
9791
 
0.1%
9781
 
0.1%
9751
 
0.1%
Other values (1283)1283
99.2%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
14591
0.1%
14581
0.1%
14571
0.1%
14561
0.1%
14551
0.1%
14541
0.1%
14521
0.1%
14511
0.1%
14481
0.1%
14471
0.1%

Id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1293
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean734.2552204
Minimum1
Maximum1460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:42.047888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile70.6
Q1368
median738
Q31100
95-th percentile1389.4
Maximum1460
Range1459
Interquartile range (IQR)732

Descriptive statistics

Standard deviation422.3313701
Coefficient of variation (CV)0.5751833401
Kurtosis-1.193977765
Mean734.2552204
Median Absolute Deviation (MAD)366
Skewness-0.01524407151
Sum949392
Variance178363.7862
MonotonicityStrictly increasing
2022-06-07T09:40:42.233883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
10091
 
0.1%
9861
 
0.1%
9841
 
0.1%
9831
 
0.1%
9821
 
0.1%
9811
 
0.1%
9801
 
0.1%
9791
 
0.1%
9761
 
0.1%
Other values (1283)1283
99.2%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
14601
0.1%
14591
0.1%
14581
0.1%
14571
0.1%
14561
0.1%
14551
0.1%
14531
0.1%
14521
0.1%
14491
0.1%
14481
0.1%

MSSubClass
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.29930394
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:42.405882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation41.22652413
Coefficient of variation (CV)0.7322741356
Kurtosis1.45105337
Mean56.29930394
Median Absolute Deviation (MAD)30
Skewness1.361513118
Sum72795
Variance1699.626292
MonotonicityNot monotonic
2022-06-07T09:40:42.540888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20483
37.4%
60275
21.3%
50126
 
9.7%
12086
 
6.7%
16061
 
4.7%
7057
 
4.4%
8057
 
4.4%
3051
 
3.9%
9028
 
2.2%
8519
 
1.5%
Other values (5)50
 
3.9%
ValueCountFrequency (%)
20483
37.4%
3051
 
3.9%
404
 
0.3%
459
 
0.7%
50126
 
9.7%
60275
21.3%
7057
 
4.4%
7513
 
1.0%
8057
 
4.4%
8519
 
1.5%
ValueCountFrequency (%)
19018
 
1.4%
1806
 
0.5%
16061
 
4.7%
12086
 
6.7%
9028
 
2.2%
8519
 
1.5%
8057
 
4.4%
7513
 
1.0%
7057
 
4.4%
60275
21.3%

MSZoning
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
RL
1022 
RM
190 
FV
 
62
RH
 
11
C (all)
 
8

Length

Max length7
Median length2
Mean length2.030935808
Min length2

Characters and Unicode

Total characters2626
Distinct characters12
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL

Common Values

ValueCountFrequency (%)
RL1022
79.0%
RM190
 
14.7%
FV62
 
4.8%
RH11
 
0.9%
C (all)8
 
0.6%

Length

2022-06-07T09:40:43.061883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:43.338566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
rl1022
78.6%
rm190
 
14.6%
fv62
 
4.8%
rh11
 
0.8%
c8
 
0.6%
all8
 
0.6%

Most occurring characters

ValueCountFrequency (%)
R1223
46.6%
L1022
38.9%
M190
 
7.2%
F62
 
2.4%
V62
 
2.4%
l16
 
0.6%
H11
 
0.4%
C8
 
0.3%
8
 
0.3%
(8
 
0.3%
Other values (2)16
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2578
98.2%
Lowercase Letter24
 
0.9%
Space Separator8
 
0.3%
Open Punctuation8
 
0.3%
Close Punctuation8
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R1223
47.4%
L1022
39.6%
M190
 
7.4%
F62
 
2.4%
V62
 
2.4%
H11
 
0.4%
C8
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
l16
66.7%
a8
33.3%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
(8
100.0%
Close Punctuation
ValueCountFrequency (%)
)8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2602
99.1%
Common24
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
R1223
47.0%
L1022
39.3%
M190
 
7.3%
F62
 
2.4%
V62
 
2.4%
l16
 
0.6%
H11
 
0.4%
C8
 
0.3%
a8
 
0.3%
Common
ValueCountFrequency (%)
8
33.3%
(8
33.3%
)8
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R1223
46.6%
L1022
38.9%
M190
 
7.2%
F62
 
2.4%
V62
 
2.4%
l16
 
0.6%
H11
 
0.4%
C8
 
0.3%
8
 
0.3%
(8
 
0.3%
Other values (2)16
 
0.6%

LotFrontage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct104
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.68368136
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:43.513572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile36
Q160
median69
Q379
95-th percentile101
Maximum313
Range292
Interquartile range (IQR)19

Descriptive statistics

Standard deviation20.55314085
Coefficient of variation (CV)0.2949491251
Kurtosis17.20192802
Mean69.68368136
Median Absolute Deviation (MAD)9
Skewness1.782388918
Sum90101
Variance422.4315988
MonotonicityNot monotonic
2022-06-07T09:40:43.707568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69245
18.9%
60120
 
9.3%
8067
 
5.2%
7063
 
4.9%
7549
 
3.8%
5048
 
3.7%
6541
 
3.2%
8537
 
2.9%
7825
 
1.9%
9021
 
1.6%
Other values (94)577
44.6%
ValueCountFrequency (%)
2117
1.3%
2419
1.5%
306
 
0.5%
325
 
0.4%
3410
0.8%
356
 
0.5%
366
 
0.5%
375
 
0.4%
381
 
0.1%
391
 
0.1%
ValueCountFrequency (%)
3131
0.1%
1821
0.1%
1741
0.1%
1681
0.1%
1521
0.1%
1491
0.1%
1441
0.1%
1401
0.1%
1371
0.1%
1342
0.2%

LotArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct957
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9688.200309
Minimum1300
Maximum29959
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:43.914567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3216.4
Q17599
median9500
Q311428
95-th percentile16208
Maximum29959
Range28659
Interquartile range (IQR)3829

Descriptive statistics

Standard deviation3853.989724
Coefficient of variation (CV)0.3978024401
Kurtosis3.153137588
Mean9688.200309
Median Absolute Deviation (MAD)1914
Skewness0.9498074462
Sum12526843
Variance14853236.79
MonotonicityNot monotonic
2022-06-07T09:40:44.105567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
960023
 
1.8%
720020
 
1.5%
600016
 
1.2%
840013
 
1.0%
900012
 
0.9%
1080011
 
0.9%
16809
 
0.7%
75008
 
0.6%
91008
 
0.6%
62407
 
0.5%
Other values (947)1166
90.2%
ValueCountFrequency (%)
13001
 
0.1%
14771
 
0.1%
15261
 
0.1%
15961
 
0.1%
16809
0.7%
18691
 
0.1%
18901
 
0.1%
19201
 
0.1%
19501
 
0.1%
19531
 
0.1%
ValueCountFrequency (%)
299591
0.1%
286981
0.1%
276501
0.1%
261781
0.1%
261421
0.1%
254191
0.1%
253391
0.1%
252861
0.1%
250951
0.1%
250001
0.1%

LotShape
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Reg
818 
IR1
437 
IR2
 
30
IR3
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3879
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowReg
3rd rowIR1
4th rowIR1
5th rowIR1

Common Values

ValueCountFrequency (%)
Reg818
63.3%
IR1437
33.8%
IR230
 
2.3%
IR38
 
0.6%

Length

2022-06-07T09:40:44.286567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:44.450568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
reg818
63.3%
ir1437
33.8%
ir230
 
2.3%
ir38
 
0.6%

Most occurring characters

ValueCountFrequency (%)
R1293
33.3%
e818
21.1%
g818
21.1%
I475
 
12.2%
1437
 
11.3%
230
 
0.8%
38
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1768
45.6%
Lowercase Letter1636
42.2%
Decimal Number475
 
12.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1437
92.0%
230
 
6.3%
38
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
R1293
73.1%
I475
 
26.9%
Lowercase Letter
ValueCountFrequency (%)
e818
50.0%
g818
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3404
87.8%
Common475
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
R1293
38.0%
e818
24.0%
g818
24.0%
I475
 
14.0%
Common
ValueCountFrequency (%)
1437
92.0%
230
 
6.3%
38
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R1293
33.3%
e818
21.1%
g818
21.1%
I475
 
12.2%
1437
 
11.3%
230
 
0.8%
38
 
0.2%

LandContour
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Lvl
1179 
Bnk
 
47
HLS
 
45
Low
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3879
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl

Common Values

ValueCountFrequency (%)
Lvl1179
91.2%
Bnk47
 
3.6%
HLS45
 
3.5%
Low22
 
1.7%

Length

2022-06-07T09:40:44.596567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:44.760567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
lvl1179
91.2%
bnk47
 
3.6%
hls45
 
3.5%
low22
 
1.7%

Most occurring characters

ValueCountFrequency (%)
L1246
32.1%
v1179
30.4%
l1179
30.4%
B47
 
1.2%
n47
 
1.2%
k47
 
1.2%
H45
 
1.2%
S45
 
1.2%
o22
 
0.6%
w22
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2496
64.3%
Uppercase Letter1383
35.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v1179
47.2%
l1179
47.2%
n47
 
1.9%
k47
 
1.9%
o22
 
0.9%
w22
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
L1246
90.1%
B47
 
3.4%
H45
 
3.3%
S45
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin3879
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L1246
32.1%
v1179
30.4%
l1179
30.4%
B47
 
1.2%
n47
 
1.2%
k47
 
1.2%
H45
 
1.2%
S45
 
1.2%
o22
 
0.6%
w22
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L1246
32.1%
v1179
30.4%
l1179
30.4%
B47
 
1.2%
n47
 
1.2%
k47
 
1.2%
H45
 
1.2%
S45
 
1.2%
o22
 
0.6%
w22
 
0.6%

LotConfig
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Inside
930 
Corner
237 
CulDSac
 
79
FR2
 
43
FR3
 
4

Length

Max length7
Median length6
Mean length5.952049497
Min length3

Characters and Unicode

Total characters7696
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInside
2nd rowFR2
3rd rowInside
4th rowCorner
5th rowFR2

Common Values

ValueCountFrequency (%)
Inside930
71.9%
Corner237
 
18.3%
CulDSac79
 
6.1%
FR243
 
3.3%
FR34
 
0.3%

Length

2022-06-07T09:40:44.910567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:45.102565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
inside930
71.9%
corner237
 
18.3%
culdsac79
 
6.1%
fr243
 
3.3%
fr34
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e1167
15.2%
n1167
15.2%
I930
12.1%
s930
12.1%
i930
12.1%
d930
12.1%
r474
6.2%
C316
 
4.1%
o237
 
3.1%
S79
 
1.0%
Other values (9)536
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6151
79.9%
Uppercase Letter1498
 
19.5%
Decimal Number47
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1167
19.0%
n1167
19.0%
s930
15.1%
i930
15.1%
d930
15.1%
r474
7.7%
o237
 
3.9%
c79
 
1.3%
a79
 
1.3%
u79
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
I930
62.1%
C316
 
21.1%
S79
 
5.3%
D79
 
5.3%
F47
 
3.1%
R47
 
3.1%
Decimal Number
ValueCountFrequency (%)
243
91.5%
34
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Latin7649
99.4%
Common47
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1167
15.3%
n1167
15.3%
I930
12.2%
s930
12.2%
i930
12.2%
d930
12.2%
r474
6.2%
C316
 
4.1%
o237
 
3.1%
S79
 
1.0%
Other values (7)489
6.4%
Common
ValueCountFrequency (%)
243
91.5%
34
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII7696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1167
15.2%
n1167
15.2%
I930
12.1%
s930
12.1%
i930
12.1%
d930
12.1%
r474
6.2%
C316
 
4.1%
o237
 
3.1%
S79
 
1.0%
Other values (9)536
7.0%

Neighborhood
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
NAmes
208 
CollgCr
145 
OldTown
99 
Somerst
82 
Gilbert
76 
Other values (20)
683 

Length

Max length7
Median length7
Mean length6.477958237
Min length5

Characters and Unicode

Total characters8376
Distinct characters38
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollgCr
2nd rowVeenker
3rd rowCollgCr
4th rowCrawfor
5th rowNoRidge

Common Values

ValueCountFrequency (%)
NAmes208
16.1%
CollgCr145
11.2%
OldTown99
 
7.7%
Somerst82
 
6.3%
Gilbert76
 
5.9%
NWAmes73
 
5.6%
Sawyer69
 
5.3%
Edwards68
 
5.3%
NridgHt63
 
4.9%
SawyerW53
 
4.1%
Other values (15)357
27.6%

Length

2022-06-07T09:40:45.268568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
names208
16.1%
collgcr145
11.2%
oldtown99
 
7.7%
somerst82
 
6.3%
gilbert76
 
5.9%
nwames73
 
5.6%
sawyer69
 
5.3%
edwards68
 
5.3%
nridght63
 
4.9%
sawyerw53
 
4.1%
Other values (15)357
27.6%

Most occurring characters

ValueCountFrequency (%)
r818
 
9.8%
e815
 
9.7%
l576
 
6.9%
o442
 
5.3%
s433
 
5.2%
m413
 
4.9%
d390
 
4.7%
N386
 
4.6%
C376
 
4.5%
w351
 
4.2%
Other values (28)3376
40.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5980
71.4%
Uppercase Letter2396
28.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r818
13.7%
e815
13.6%
l576
9.6%
o442
 
7.4%
s433
 
7.2%
m413
 
6.9%
d390
 
6.5%
w351
 
5.9%
i302
 
5.1%
t302
 
5.1%
Other values (10)1138
19.0%
Uppercase Letter
ValueCountFrequency (%)
N386
16.1%
C376
15.7%
S312
13.0%
A281
11.7%
T161
6.7%
W146
 
6.1%
O128
 
5.3%
B102
 
4.3%
R91
 
3.8%
G76
 
3.2%
Other values (8)337
14.1%

Most occurring scripts

ValueCountFrequency (%)
Latin8376
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r818
 
9.8%
e815
 
9.7%
l576
 
6.9%
o442
 
5.3%
s433
 
5.2%
m413
 
4.9%
d390
 
4.7%
N386
 
4.6%
C376
 
4.5%
w351
 
4.2%
Other values (28)3376
40.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r818
 
9.8%
e815
 
9.7%
l576
 
6.9%
o442
 
5.3%
s433
 
5.2%
m413
 
4.9%
d390
 
4.7%
N386
 
4.6%
C376
 
4.5%
w351
 
4.2%
Other values (28)3376
40.3%

BldgType
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
1Fam
1096 
TwnhsE
112 
Twnhs
 
38
Duplex
 
28
2fmCon
 
19

Length

Max length6
Median length4
Mean length4.275328693
Min length4

Characters and Unicode

Total characters5528
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam

Common Values

ValueCountFrequency (%)
1Fam1096
84.8%
TwnhsE112
 
8.7%
Twnhs38
 
2.9%
Duplex28
 
2.2%
2fmCon19
 
1.5%

Length

2022-06-07T09:40:45.455570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:45.646567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1fam1096
84.8%
twnhse112
 
8.7%
twnhs38
 
2.9%
duplex28
 
2.2%
2fmcon19
 
1.5%

Most occurring characters

ValueCountFrequency (%)
m1115
20.2%
11096
19.8%
a1096
19.8%
F1096
19.8%
n169
 
3.1%
T150
 
2.7%
w150
 
2.7%
h150
 
2.7%
s150
 
2.7%
E112
 
2.0%
Other values (10)244
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3008
54.4%
Uppercase Letter1405
25.4%
Decimal Number1115
 
20.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m1115
37.1%
a1096
36.4%
n169
 
5.6%
w150
 
5.0%
h150
 
5.0%
s150
 
5.0%
l28
 
0.9%
x28
 
0.9%
e28
 
0.9%
p28
 
0.9%
Other values (3)66
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
F1096
78.0%
T150
 
10.7%
E112
 
8.0%
D28
 
2.0%
C19
 
1.4%
Decimal Number
ValueCountFrequency (%)
11096
98.3%
219
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin4413
79.8%
Common1115
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
m1115
25.3%
a1096
24.8%
F1096
24.8%
n169
 
3.8%
T150
 
3.4%
w150
 
3.4%
h150
 
3.4%
s150
 
3.4%
E112
 
2.5%
l28
 
0.6%
Other values (8)197
 
4.5%
Common
ValueCountFrequency (%)
11096
98.3%
219
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m1115
20.2%
11096
19.8%
a1096
19.8%
F1096
19.8%
n169
 
3.1%
T150
 
2.7%
w150
 
2.7%
h150
 
2.7%
s150
 
2.7%
E112
 
2.0%
Other values (10)244
 
4.4%

HouseStyle
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
1Story
636 
2Story
407 
1.5Fin
130 
SLvl
64 
SFoyer
 
30
Other values (3)
 
26

Length

Max length6
Median length6
Mean length5.901005414
Min length4

Characters and Unicode

Total characters7630
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2Story
2nd row1Story
3rd row2Story
4th row2Story
5th row2Story

Common Values

ValueCountFrequency (%)
1Story636
49.2%
2Story407
31.5%
1.5Fin130
 
10.1%
SLvl64
 
4.9%
SFoyer30
 
2.3%
1.5Unf11
 
0.9%
2.5Unf10
 
0.8%
2.5Fin5
 
0.4%

Length

2022-06-07T09:40:45.816566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:46.015578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1story636
49.2%
2story407
31.5%
1.5fin130
 
10.1%
slvl64
 
4.9%
sfoyer30
 
2.3%
1.5unf11
 
0.9%
2.5unf10
 
0.8%
2.5fin5
 
0.4%

Most occurring characters

ValueCountFrequency (%)
S1137
14.9%
o1073
14.1%
r1073
14.1%
y1073
14.1%
t1043
13.7%
1777
10.2%
2422
 
5.5%
F165
 
2.2%
5156
 
2.0%
.156
 
2.0%
Other values (8)555
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4732
62.0%
Uppercase Letter1387
 
18.2%
Decimal Number1355
 
17.8%
Other Punctuation156
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1073
22.7%
r1073
22.7%
y1073
22.7%
t1043
22.0%
n156
 
3.3%
i135
 
2.9%
v64
 
1.4%
l64
 
1.4%
e30
 
0.6%
f21
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
S1137
82.0%
F165
 
11.9%
L64
 
4.6%
U21
 
1.5%
Decimal Number
ValueCountFrequency (%)
1777
57.3%
2422
31.1%
5156
 
11.5%
Other Punctuation
ValueCountFrequency (%)
.156
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6119
80.2%
Common1511
 
19.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
S1137
18.6%
o1073
17.5%
r1073
17.5%
y1073
17.5%
t1043
17.0%
F165
 
2.7%
n156
 
2.5%
i135
 
2.2%
L64
 
1.0%
v64
 
1.0%
Other values (4)136
 
2.2%
Common
ValueCountFrequency (%)
1777
51.4%
2422
27.9%
5156
 
10.3%
.156
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S1137
14.9%
o1073
14.1%
r1073
14.1%
y1073
14.1%
t1043
13.7%
1777
10.2%
2422
 
5.5%
F165
 
2.2%
5156
 
2.0%
.156
 
2.0%
Other values (8)555
7.3%

OverallQual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.158546017
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:46.186566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.258561414
Coefficient of variation (CV)0.2043601542
Kurtosis-0.2385307004
Mean6.158546017
Median Absolute Deviation (MAD)1
Skewness0.1844935508
Sum7963
Variance1.583976832
MonotonicityNot monotonic
2022-06-07T09:40:46.334567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
6355
27.5%
5346
26.8%
7308
23.8%
8157
12.1%
479
 
6.1%
933
 
2.6%
38
 
0.6%
105
 
0.4%
22
 
0.2%
ValueCountFrequency (%)
22
 
0.2%
38
 
0.6%
479
 
6.1%
5346
26.8%
6355
27.5%
7308
23.8%
8157
12.1%
933
 
2.6%
105
 
0.4%
ValueCountFrequency (%)
105
 
0.4%
933
 
2.6%
8157
12.1%
7308
23.8%
6355
27.5%
5346
26.8%
479
 
6.1%
38
 
0.6%
22
 
0.2%

OverallCond
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.606341841
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:46.485566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q15
median5
Q36
95-th percentile8
Maximum9
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.082018809
Coefficient of variation (CV)0.1929990785
Kurtosis0.9619512987
Mean5.606341841
Median Absolute Deviation (MAD)0
Skewness0.8629833387
Sum7249
Variance1.170764703
MonotonicityNot monotonic
2022-06-07T09:40:46.631566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5735
56.8%
6230
 
17.8%
7180
 
13.9%
867
 
5.2%
444
 
3.4%
919
 
1.5%
315
 
1.2%
23
 
0.2%
ValueCountFrequency (%)
23
 
0.2%
315
 
1.2%
444
 
3.4%
5735
56.8%
6230
 
17.8%
7180
 
13.9%
867
 
5.2%
919
 
1.5%
ValueCountFrequency (%)
919
 
1.5%
867
 
5.2%
7180
 
13.9%
6230
 
17.8%
5735
56.8%
444
 
3.4%
315
 
1.2%
23
 
0.2%

YearBuilt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct109
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1972.584687
Minimum1880
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:46.820567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1880
5-th percentile1918.6
Q11955
median1975
Q32001
95-th percentile2007
Maximum2010
Range130
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.5459444
Coefficient of variation (CV)0.01497828945
Kurtosis-0.3709830501
Mean1972.584687
Median Absolute Deviation (MAD)24
Skewness-0.6580309947
Sum2550552
Variance872.9628303
MonotonicityNot monotonic
2022-06-07T09:40:47.033567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200561
 
4.7%
200659
 
4.6%
200453
 
4.1%
200744
 
3.4%
200340
 
3.1%
197633
 
2.6%
197729
 
2.2%
199825
 
1.9%
192025
 
1.9%
199925
 
1.9%
Other values (99)899
69.5%
ValueCountFrequency (%)
18804
0.3%
18821
 
0.1%
18852
 
0.2%
18902
 
0.2%
18921
 
0.1%
18931
 
0.1%
18981
 
0.1%
19008
0.6%
19041
 
0.1%
19051
 
0.1%
ValueCountFrequency (%)
20101
 
0.1%
200916
 
1.2%
200816
 
1.2%
200744
3.4%
200659
4.6%
200561
4.7%
200453
4.1%
200340
3.1%
200220
 
1.5%
200119
 
1.5%

RoofStyle
Categorical

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Gable
1018 
Hip
248 
Gambrel
 
10
Flat
 
9
Mansard
 
6

Length

Max length7
Median length5
Mean length4.632637278
Min length3

Characters and Unicode

Total characters5990
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable

Common Values

ValueCountFrequency (%)
Gable1018
78.7%
Hip248
 
19.2%
Gambrel10
 
0.8%
Flat9
 
0.7%
Mansard6
 
0.5%
Shed2
 
0.2%

Length

2022-06-07T09:40:47.218568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:47.398571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
gable1018
78.7%
hip248
 
19.2%
gambrel10
 
0.8%
flat9
 
0.7%
mansard6
 
0.5%
shed2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a1049
17.5%
l1037
17.3%
e1030
17.2%
G1028
17.2%
b1028
17.2%
H248
 
4.1%
i248
 
4.1%
p248
 
4.1%
r16
 
0.3%
m10
 
0.2%
Other values (8)48
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4697
78.4%
Uppercase Letter1293
 
21.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1049
22.3%
l1037
22.1%
e1030
21.9%
b1028
21.9%
i248
 
5.3%
p248
 
5.3%
r16
 
0.3%
m10
 
0.2%
t9
 
0.2%
d8
 
0.2%
Other values (3)14
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
G1028
79.5%
H248
 
19.2%
F9
 
0.7%
M6
 
0.5%
S2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin5990
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1049
17.5%
l1037
17.3%
e1030
17.2%
G1028
17.2%
b1028
17.2%
H248
 
4.1%
i248
 
4.1%
p248
 
4.1%
r16
 
0.3%
m10
 
0.2%
Other values (8)48
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1049
17.5%
l1037
17.3%
e1030
17.2%
G1028
17.2%
b1028
17.2%
H248
 
4.1%
i248
 
4.1%
p248
 
4.1%
r16
 
0.3%
m10
 
0.2%
Other values (8)48
 
0.8%

Exterior1st
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
VinylSd
470 
HdBoard
208 
MetalSd
199 
Wd Sdng
176 
Plywood
95 
Other values (9)
145 

Length

Max length7
Median length7
Mean length6.98066512
Min length5

Characters and Unicode

Total characters9026
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd470
36.3%
HdBoard208
16.1%
MetalSd199
15.4%
Wd Sdng176
 
13.6%
Plywood95
 
7.3%
CemntBd45
 
3.5%
BrkFace41
 
3.2%
Stucco20
 
1.5%
WdShing19
 
1.5%
AsbShng15
 
1.2%
Other values (4)5
 
0.4%

Length

2022-06-07T09:40:47.570328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd470
32.0%
hdboard208
14.2%
metalsd199
13.5%
wd176
 
12.0%
sdng176
 
12.0%
plywood95
 
6.5%
cemntbd45
 
3.1%
brkface41
 
2.8%
stucco20
 
1.4%
wdshing19
 
1.3%
Other values (5)20
 
1.4%

Most occurring characters

ValueCountFrequency (%)
d1596
17.7%
S902
 
10.0%
l765
 
8.5%
n727
 
8.1%
y565
 
6.3%
i489
 
5.4%
V470
 
5.2%
a448
 
5.0%
o422
 
4.7%
B296
 
3.3%
Other values (21)2346
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6381
70.7%
Uppercase Letter2469
 
27.4%
Space Separator176
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d1596
25.0%
l765
12.0%
n727
11.4%
y565
 
8.9%
i489
 
7.7%
a448
 
7.0%
o422
 
6.6%
e287
 
4.5%
t267
 
4.2%
r250
 
3.9%
Other values (9)565
 
8.9%
Uppercase Letter
ValueCountFrequency (%)
S902
36.5%
V470
19.0%
B296
 
12.0%
H208
 
8.4%
M199
 
8.1%
W195
 
7.9%
P95
 
3.8%
C47
 
1.9%
F41
 
1.7%
A15
 
0.6%
Space Separator
ValueCountFrequency (%)
176
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8850
98.1%
Common176
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
d1596
18.0%
S902
10.2%
l765
 
8.6%
n727
 
8.2%
y565
 
6.4%
i489
 
5.5%
V470
 
5.3%
a448
 
5.1%
o422
 
4.8%
B296
 
3.3%
Other values (20)2170
24.5%
Common
ValueCountFrequency (%)
176
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d1596
17.7%
S902
 
10.0%
l765
 
8.5%
n727
 
8.1%
y565
 
6.3%
i489
 
5.4%
V470
 
5.2%
a448
 
5.0%
o422
 
4.7%
B296
 
3.3%
Other values (21)2346
26.0%

MasVnrType
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
None
749 
BrkFace
418 
Stone
113 
BrkCmn
 
13

Length

Max length7
Median length4
Mean length5.07733952
Min length4

Characters and Unicode

Total characters6565
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrkFace
2nd rowNone
3rd rowBrkFace
4th rowNone
5th rowBrkFace

Common Values

ValueCountFrequency (%)
None749
57.9%
BrkFace418
32.3%
Stone113
 
8.7%
BrkCmn13
 
1.0%

Length

2022-06-07T09:40:47.743332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:47.908329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
none749
57.9%
brkface418
32.3%
stone113
 
8.7%
brkcmn13
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e1280
19.5%
n875
13.3%
o862
13.1%
N749
11.4%
B431
 
6.6%
r431
 
6.6%
k431
 
6.6%
F418
 
6.4%
a418
 
6.4%
c418
 
6.4%
Other values (4)252
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4841
73.7%
Uppercase Letter1724
 
26.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1280
26.4%
n875
18.1%
o862
17.8%
r431
 
8.9%
k431
 
8.9%
a418
 
8.6%
c418
 
8.6%
t113
 
2.3%
m13
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N749
43.4%
B431
25.0%
F418
24.2%
S113
 
6.6%
C13
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin6565
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1280
19.5%
n875
13.3%
o862
13.1%
N749
11.4%
B431
 
6.6%
r431
 
6.6%
k431
 
6.6%
F418
 
6.4%
a418
 
6.4%
c418
 
6.4%
Other values (4)252
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII6565
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1280
19.5%
n875
13.3%
o862
13.1%
N749
11.4%
B431
 
6.6%
r431
 
6.6%
k431
 
6.6%
F418
 
6.4%
a418
 
6.4%
c418
 
6.4%
Other values (4)252
 
3.8%

ExterQual
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
TA
790 
Gd
462 
Ex
 
34
Fa
 
7

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2586
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA790
61.1%
Gd462
35.7%
Ex34
 
2.6%
Fa7
 
0.5%

Length

2022-06-07T09:40:48.063330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:48.223332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ta790
61.1%
gd462
35.7%
ex34
 
2.6%
fa7
 
0.5%

Most occurring characters

ValueCountFrequency (%)
T790
30.5%
A790
30.5%
G462
17.9%
d462
17.9%
E34
 
1.3%
x34
 
1.3%
F7
 
0.3%
a7
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2083
80.5%
Lowercase Letter503
 
19.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T790
37.9%
A790
37.9%
G462
22.2%
E34
 
1.6%
F7
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
d462
91.8%
x34
 
6.8%
a7
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin2586
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T790
30.5%
A790
30.5%
G462
17.9%
d462
17.9%
E34
 
1.3%
x34
 
1.3%
F7
 
0.3%
a7
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T790
30.5%
A790
30.5%
G462
17.9%
d462
17.9%
E34
 
1.3%
x34
 
1.3%
F7
 
0.3%
a7
 
0.3%

ExterCond
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
TA
1142 
Gd
133 
Fa
 
16
Ex
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2586
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA1142
88.3%
Gd133
 
10.3%
Fa16
 
1.2%
Ex2
 
0.2%

Length

2022-06-07T09:40:48.371329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:48.538328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ta1142
88.3%
gd133
 
10.3%
fa16
 
1.2%
ex2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T1142
44.2%
A1142
44.2%
G133
 
5.1%
d133
 
5.1%
F16
 
0.6%
a16
 
0.6%
E2
 
0.1%
x2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2435
94.2%
Lowercase Letter151
 
5.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T1142
46.9%
A1142
46.9%
G133
 
5.5%
F16
 
0.7%
E2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
d133
88.1%
a16
 
10.6%
x2
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2586
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T1142
44.2%
A1142
44.2%
G133
 
5.1%
d133
 
5.1%
F16
 
0.6%
a16
 
0.6%
E2
 
0.1%
x2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T1142
44.2%
A1142
44.2%
G133
 
5.1%
d133
 
5.1%
F16
 
0.6%
a16
 
0.6%
E2
 
0.1%
x2
 
0.1%

Foundation
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
PConc
592 
CBlock
565 
BrkTil
127 
Stone
 
6
Wood
 
3

Length

Max length6
Median length6
Mean length5.532869296
Min length4

Characters and Unicode

Total characters7154
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc

Common Values

ValueCountFrequency (%)
PConc592
45.8%
CBlock565
43.7%
BrkTil127
 
9.8%
Stone6
 
0.5%
Wood3
 
0.2%

Length

2022-06-07T09:40:48.691084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:48.875083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
pconc592
45.8%
cblock565
43.7%
brktil127
 
9.8%
stone6
 
0.5%
wood3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o1169
16.3%
C1157
16.2%
c1157
16.2%
B692
9.7%
l692
9.7%
k692
9.7%
n598
8.4%
P592
8.3%
r127
 
1.8%
T127
 
1.8%
Other values (6)151
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4577
64.0%
Uppercase Letter2577
36.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1169
25.5%
c1157
25.3%
l692
15.1%
k692
15.1%
n598
13.1%
r127
 
2.8%
i127
 
2.8%
t6
 
0.1%
e6
 
0.1%
d3
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
C1157
44.9%
B692
26.9%
P592
23.0%
T127
 
4.9%
S6
 
0.2%
W3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin7154
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o1169
16.3%
C1157
16.2%
c1157
16.2%
B692
9.7%
l692
9.7%
k692
9.7%
n598
8.4%
P592
8.3%
r127
 
1.8%
T127
 
1.8%
Other values (6)151
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII7154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o1169
16.3%
C1157
16.2%
c1157
16.2%
B692
9.7%
l692
9.7%
k692
9.7%
n598
8.4%
P592
8.3%
r127
 
1.8%
T127
 
1.8%
Other values (6)151
 
2.1%

BsmtQual
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
TA
585 
Gd
580 
Ex
96 
Fa
 
32

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2586
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowGd
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA585
45.2%
Gd580
44.9%
Ex96
 
7.4%
Fa32
 
2.5%

Length

2022-06-07T09:40:49.026086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:49.204090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ta585
45.2%
gd580
44.9%
ex96
 
7.4%
fa32
 
2.5%

Most occurring characters

ValueCountFrequency (%)
T585
22.6%
A585
22.6%
G580
22.4%
d580
22.4%
E96
 
3.7%
x96
 
3.7%
F32
 
1.2%
a32
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1878
72.6%
Lowercase Letter708
 
27.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T585
31.2%
A585
31.2%
G580
30.9%
E96
 
5.1%
F32
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
d580
81.9%
x96
 
13.6%
a32
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Latin2586
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T585
22.6%
A585
22.6%
G580
22.4%
d580
22.4%
E96
 
3.7%
x96
 
3.7%
F32
 
1.2%
a32
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T585
22.6%
A585
22.6%
G580
22.4%
d580
22.4%
E96
 
3.7%
x96
 
3.7%
F32
 
1.2%
a32
 
1.2%

BsmtCond
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
TA
1194 
Gd
 
60
Fa
 
38
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2586
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA

Common Values

ValueCountFrequency (%)
TA1194
92.3%
Gd60
 
4.6%
Fa38
 
2.9%
Po1
 
0.1%

Length

2022-06-07T09:40:49.344085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:49.517090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ta1194
92.3%
gd60
 
4.6%
fa38
 
2.9%
po1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T1194
46.2%
A1194
46.2%
G60
 
2.3%
d60
 
2.3%
F38
 
1.5%
a38
 
1.5%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2487
96.2%
Lowercase Letter99
 
3.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T1194
48.0%
A1194
48.0%
G60
 
2.4%
F38
 
1.5%
P1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
d60
60.6%
a38
38.4%
o1
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2586
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T1194
46.2%
A1194
46.2%
G60
 
2.3%
d60
 
2.3%
F38
 
1.5%
a38
 
1.5%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T1194
46.2%
A1194
46.2%
G60
 
2.3%
d60
 
2.3%
F38
 
1.5%
a38
 
1.5%
P1
 
< 0.1%
o1
 
< 0.1%

BsmtExposure
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
No
879 
Av
203 
Mn
108 
Gd
103 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2586
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv

Common Values

ValueCountFrequency (%)
No879
68.0%
Av203
 
15.7%
Mn108
 
8.4%
Gd103
 
8.0%

Length

2022-06-07T09:40:49.648086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:49.810085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no879
68.0%
av203
 
15.7%
mn108
 
8.4%
gd103
 
8.0%

Most occurring characters

ValueCountFrequency (%)
N879
34.0%
o879
34.0%
A203
 
7.8%
v203
 
7.8%
M108
 
4.2%
n108
 
4.2%
G103
 
4.0%
d103
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1293
50.0%
Lowercase Letter1293
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N879
68.0%
A203
 
15.7%
M108
 
8.4%
G103
 
8.0%
Lowercase Letter
ValueCountFrequency (%)
o879
68.0%
v203
 
15.7%
n108
 
8.4%
d103
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2586
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N879
34.0%
o879
34.0%
A203
 
7.8%
v203
 
7.8%
M108
 
4.2%
n108
 
4.2%
G103
 
4.0%
d103
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N879
34.0%
o879
34.0%
A203
 
7.8%
v203
 
7.8%
M108
 
4.2%
n108
 
4.2%
G103
 
4.0%
d103
 
4.0%

BsmtFinType1
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Unf
386 
GLQ
376 
ALQ
205 
BLQ
137 
Rec
121 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3879
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ

Common Values

ValueCountFrequency (%)
Unf386
29.9%
GLQ376
29.1%
ALQ205
15.9%
BLQ137
 
10.6%
Rec121
 
9.4%
LwQ68
 
5.3%

Length

2022-06-07T09:40:49.953085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:50.131083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
unf386
29.9%
glq376
29.1%
alq205
15.9%
blq137
 
10.6%
rec121
 
9.4%
lwq68
 
5.3%

Most occurring characters

ValueCountFrequency (%)
L786
20.3%
Q786
20.3%
U386
10.0%
n386
10.0%
f386
10.0%
G376
9.7%
A205
 
5.3%
B137
 
3.5%
R121
 
3.1%
e121
 
3.1%
Other values (2)189
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2797
72.1%
Lowercase Letter1082
 
27.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L786
28.1%
Q786
28.1%
U386
13.8%
G376
13.4%
A205
 
7.3%
B137
 
4.9%
R121
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
n386
35.7%
f386
35.7%
e121
 
11.2%
c121
 
11.2%
w68
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Latin3879
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L786
20.3%
Q786
20.3%
U386
10.0%
n386
10.0%
f386
10.0%
G376
9.7%
A205
 
5.3%
B137
 
3.5%
R121
 
3.1%
e121
 
3.1%
Other values (2)189
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L786
20.3%
Q786
20.3%
U386
10.0%
n386
10.0%
f386
10.0%
G376
9.7%
A205
 
5.3%
B137
 
3.5%
R121
 
3.1%
e121
 
3.1%
Other values (2)189
 
4.9%

BsmtFinSF1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct594
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean440.0680588
Minimum0
Maximum1880
Zeros386
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:50.322085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median400
Q3706
95-th percentile1218.8
Maximum1880
Range1880
Interquartile range (IQR)706

Descriptive statistics

Standard deviation414.3547093
Coefficient of variation (CV)0.9415696072
Kurtosis-0.4054378406
Mean440.0680588
Median Absolute Deviation (MAD)387
Skewness0.6457144579
Sum569008
Variance171689.8251
MonotonicityNot monotonic
2022-06-07T09:40:50.513086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0386
29.9%
2412
 
0.9%
169
 
0.7%
205
 
0.4%
9365
 
0.4%
6625
 
0.4%
6865
 
0.4%
3124
 
0.3%
6414
 
0.3%
3844
 
0.3%
Other values (584)854
66.0%
ValueCountFrequency (%)
0386
29.9%
21
 
0.1%
169
 
0.7%
205
 
0.4%
2412
 
0.9%
251
 
0.1%
271
 
0.1%
283
 
0.2%
331
 
0.1%
351
 
0.1%
ValueCountFrequency (%)
18801
0.1%
16961
0.1%
16461
0.1%
16361
0.1%
16191
0.1%
16061
0.1%
15731
0.1%
15721
0.1%
15672
0.2%
15401
0.1%

BsmtFinType2
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Unf
1137 
Rec
 
50
LwQ
 
46
BLQ
 
31
ALQ
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3879
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnf
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowUnf

Common Values

ValueCountFrequency (%)
Unf1137
87.9%
Rec50
 
3.9%
LwQ46
 
3.6%
BLQ31
 
2.4%
ALQ18
 
1.4%
GLQ11
 
0.9%

Length

2022-06-07T09:40:50.719083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:50.884086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
unf1137
87.9%
rec50
 
3.9%
lwq46
 
3.6%
blq31
 
2.4%
alq18
 
1.4%
glq11
 
0.9%

Most occurring characters

ValueCountFrequency (%)
U1137
29.3%
n1137
29.3%
f1137
29.3%
L106
 
2.7%
Q106
 
2.7%
R50
 
1.3%
e50
 
1.3%
c50
 
1.3%
w46
 
1.2%
B31
 
0.8%
Other values (2)29
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2420
62.4%
Uppercase Letter1459
37.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U1137
77.9%
L106
 
7.3%
Q106
 
7.3%
R50
 
3.4%
B31
 
2.1%
A18
 
1.2%
G11
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
n1137
47.0%
f1137
47.0%
e50
 
2.1%
c50
 
2.1%
w46
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin3879
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U1137
29.3%
n1137
29.3%
f1137
29.3%
L106
 
2.7%
Q106
 
2.7%
R50
 
1.3%
e50
 
1.3%
c50
 
1.3%
w46
 
1.2%
B31
 
0.8%
Other values (2)29
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U1137
29.3%
n1137
29.3%
f1137
29.3%
L106
 
2.7%
Q106
 
2.7%
R50
 
1.3%
e50
 
1.3%
c50
 
1.3%
w46
 
1.2%
B31
 
0.8%
Other values (2)29
 
0.7%

BsmtUnfSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct744
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean579.7231245
Minimum0
Maximum2336
Zeros72
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:51.067087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1248
median490
Q3814
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)566

Descriptive statistics

Standard deviation434.7603828
Coefficient of variation (CV)0.7499448692
Kurtosis0.4101460408
Mean579.7231245
Median Absolute Deviation (MAD)280
Skewness0.9090468506
Sum749582
Variance189016.5905
MonotonicityNot monotonic
2022-06-07T09:40:51.244086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
072
 
5.6%
7288
 
0.6%
3846
 
0.5%
6256
 
0.5%
6006
 
0.5%
2806
 
0.5%
5726
 
0.5%
3006
 
0.5%
3905
 
0.4%
6725
 
0.4%
Other values (734)1167
90.3%
ValueCountFrequency (%)
072
5.6%
141
 
0.1%
151
 
0.1%
232
 
0.2%
261
 
0.1%
291
 
0.1%
321
 
0.1%
351
 
0.1%
364
 
0.3%
381
 
0.1%
ValueCountFrequency (%)
23361
0.1%
21531
0.1%
20461
0.1%
20421
0.1%
20021
0.1%
19351
0.1%
19071
0.1%
19051
0.1%
18691
0.1%
18361
0.1%

TotalBsmtSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct672
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1068.337974
Minimum105
Maximum2524
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:51.425086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile600
Q1816
median1004
Q31284
95-th percentile1710
Maximum2524
Range2419
Interquartile range (IQR)468

Descriptive statistics

Standard deviation351.1871588
Coefficient of variation (CV)0.3287229018
Kurtosis0.2610175375
Mean1068.337974
Median Absolute Deviation (MAD)221
Skewness0.6587583123
Sum1381361
Variance123332.4205
MonotonicityNot monotonic
2022-06-07T09:40:51.641086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86431
 
2.4%
67215
 
1.2%
91214
 
1.1%
104013
 
1.0%
81612
 
0.9%
76812
 
0.9%
84811
 
0.9%
89411
 
0.9%
72811
 
0.9%
83210
 
0.8%
Other values (662)1153
89.2%
ValueCountFrequency (%)
1051
 
0.1%
1901
 
0.1%
2642
 
0.2%
2901
 
0.1%
3601
 
0.1%
3721
 
0.1%
3846
0.5%
4081
 
0.1%
4402
 
0.2%
4581
 
0.1%
ValueCountFrequency (%)
25241
0.1%
23921
0.1%
22231
0.1%
22171
0.1%
21581
0.1%
21531
0.1%
21361
0.1%
21101
0.1%
21091
0.1%
20781
0.1%

HeatingQC
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Ex
670 
TA
374 
Gd
214 
Fa
 
34
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2586
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx

Common Values

ValueCountFrequency (%)
Ex670
51.8%
TA374
28.9%
Gd214
 
16.6%
Fa34
 
2.6%
Po1
 
0.1%

Length

2022-06-07T09:40:51.811085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:51.974090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ex670
51.8%
ta374
28.9%
gd214
 
16.6%
fa34
 
2.6%
po1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E670
25.9%
x670
25.9%
T374
14.5%
A374
14.5%
G214
 
8.3%
d214
 
8.3%
F34
 
1.3%
a34
 
1.3%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1667
64.5%
Lowercase Letter919
35.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E670
40.2%
T374
22.4%
A374
22.4%
G214
 
12.8%
F34
 
2.0%
P1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
x670
72.9%
d214
 
23.3%
a34
 
3.7%
o1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin2586
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E670
25.9%
x670
25.9%
T374
14.5%
A374
14.5%
G214
 
8.3%
d214
 
8.3%
F34
 
1.3%
a34
 
1.3%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E670
25.9%
x670
25.9%
T374
14.5%
A374
14.5%
G214
 
8.3%
d214
 
8.3%
F34
 
1.3%
a34
 
1.3%
P1
 
< 0.1%
o1
 
< 0.1%

1stFlrSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct695
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1150.75174
Minimum438
Maximum2898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:52.158085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum438
5-th percentile684
Q1889
median1088
Q31370
95-th percentile1776
Maximum2898
Range2460
Interquartile range (IQR)481

Descriptive statistics

Standard deviation349.6027083
Coefficient of variation (CV)0.3038037624
Kurtosis0.4789031222
Mean1150.75174
Median Absolute Deviation (MAD)227
Skewness0.7366620454
Sum1487922
Variance122222.0536
MonotonicityNot monotonic
2022-06-07T09:40:52.368086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86421
 
1.6%
91213
 
1.0%
89412
 
0.9%
84812
 
0.9%
10409
 
0.7%
6729
 
0.7%
8168
 
0.6%
9607
 
0.5%
4837
 
0.5%
8327
 
0.5%
Other values (685)1188
91.9%
ValueCountFrequency (%)
4381
 
0.1%
4801
 
0.1%
4837
0.5%
5204
0.3%
5251
 
0.1%
5261
 
0.1%
5361
 
0.1%
5463
0.2%
5511
 
0.1%
5611
 
0.1%
ValueCountFrequency (%)
28981
0.1%
25241
0.1%
23921
0.1%
22591
0.1%
22231
0.1%
22171
0.1%
22071
0.1%
21961
0.1%
21581
0.1%
21561
0.1%

GrLivArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct787
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1502.778809
Minimum438
Maximum3493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:52.587086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum438
5-th percentile864
Q11148
median1468
Q31762
95-th percentile2377.6
Maximum3493
Range3055
Interquartile range (IQR)614

Descriptive statistics

Standard deviation462.8135543
Coefficient of variation (CV)0.3079718396
Kurtosis0.6327836963
Mean1502.778809
Median Absolute Deviation (MAD)307
Skewness0.7203497005
Sum1943093
Variance214196.386
MonotonicityNot monotonic
2022-06-07T09:40:52.787088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86418
 
1.4%
89411
 
0.9%
145610
 
0.8%
84810
 
0.8%
9128
 
0.6%
9877
 
0.5%
10407
 
0.5%
8167
 
0.5%
12007
 
0.5%
10927
 
0.5%
Other values (777)1201
92.9%
ValueCountFrequency (%)
4381
0.1%
4801
0.1%
5201
0.1%
6161
0.1%
6302
0.2%
6722
0.2%
6911
0.1%
6941
0.1%
7201
0.1%
7472
0.2%
ValueCountFrequency (%)
34931
0.1%
34471
0.1%
32221
0.1%
31941
0.1%
31121
0.1%
30821
0.1%
29781
0.1%
28981
0.1%
28722
0.2%
28101
0.1%

BsmtFullBath
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
754 
1
530 
2
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1293
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0754
58.3%
1530
41.0%
29
 
0.7%

Length

2022-06-07T09:40:52.972085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:53.133085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0754
58.3%
1530
41.0%
29
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0754
58.3%
1530
41.0%
29
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0754
58.3%
1530
41.0%
29
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common1293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0754
58.3%
1530
41.0%
29
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0754
58.3%
1530
41.0%
29
 
0.7%

FullBath
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
2
696 
1
573 
3
 
17
0
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1293
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2696
53.8%
1573
44.3%
317
 
1.3%
07
 
0.5%

Length

2022-06-07T09:40:53.264086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:53.421735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2696
53.8%
1573
44.3%
317
 
1.3%
07
 
0.5%

Most occurring characters

ValueCountFrequency (%)
2696
53.8%
1573
44.3%
317
 
1.3%
07
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2696
53.8%
1573
44.3%
317
 
1.3%
07
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common1293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2696
53.8%
1573
44.3%
317
 
1.3%
07
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2696
53.8%
1573
44.3%
317
 
1.3%
07
 
0.5%

HalfBath
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
790 
1
494 
2
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1293
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0790
61.1%
1494
38.2%
29
 
0.7%

Length

2022-06-07T09:40:53.566731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:53.730731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0790
61.1%
1494
38.2%
29
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0790
61.1%
1494
38.2%
29
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0790
61.1%
1494
38.2%
29
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common1293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0790
61.1%
1494
38.2%
29
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0790
61.1%
1494
38.2%
29
 
0.7%

BedroomAbvGr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.857695282
Minimum0
Maximum6
Zeros5
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:53.854731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7668721058
Coefficient of variation (CV)0.2683533512
Kurtosis1.504044815
Mean2.857695282
Median Absolute Deviation (MAD)0
Skewness-0.02045051593
Sum3695
Variance0.5880928266
MonotonicityNot monotonic
2022-06-07T09:40:53.987732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3747
57.8%
2309
23.9%
4172
 
13.3%
139
 
3.0%
517
 
1.3%
05
 
0.4%
64
 
0.3%
ValueCountFrequency (%)
05
 
0.4%
139
 
3.0%
2309
23.9%
3747
57.8%
4172
 
13.3%
517
 
1.3%
64
 
0.3%
ValueCountFrequency (%)
64
 
0.3%
517
 
1.3%
4172
 
13.3%
3747
57.8%
2309
23.9%
139
 
3.0%
05
 
0.4%

KitchenQual
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
TA
640 
Gd
555 
Ex
75 
Fa
 
23

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2586
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd

Common Values

ValueCountFrequency (%)
TA640
49.5%
Gd555
42.9%
Ex75
 
5.8%
Fa23
 
1.8%

Length

2022-06-07T09:40:54.147729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:54.317729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ta640
49.5%
gd555
42.9%
ex75
 
5.8%
fa23
 
1.8%

Most occurring characters

ValueCountFrequency (%)
T640
24.7%
A640
24.7%
G555
21.5%
d555
21.5%
E75
 
2.9%
x75
 
2.9%
F23
 
0.9%
a23
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1933
74.7%
Lowercase Letter653
 
25.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T640
33.1%
A640
33.1%
G555
28.7%
E75
 
3.9%
F23
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
d555
85.0%
x75
 
11.5%
a23
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin2586
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T640
24.7%
A640
24.7%
G555
21.5%
d555
21.5%
E75
 
2.9%
x75
 
2.9%
F23
 
0.9%
a23
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T640
24.7%
A640
24.7%
G555
21.5%
d555
21.5%
E75
 
2.9%
x75
 
2.9%
F23
 
0.9%
a23
 
0.9%

TotRmsAbvGrd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.474091261
Minimum3
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:54.452731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q15
median6
Q37
95-th percentile9
Maximum12
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.506534286
Coefficient of variation (CV)0.2327020466
Kurtosis0.6625746595
Mean6.474091261
Median Absolute Deviation (MAD)1
Skewness0.5850438655
Sum8371
Variance2.269645555
MonotonicityNot monotonic
2022-06-07T09:40:54.588730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6378
29.2%
7302
23.4%
5242
18.7%
8166
12.8%
479
 
6.1%
963
 
4.9%
1034
 
2.6%
1112
 
0.9%
312
 
0.9%
125
 
0.4%
ValueCountFrequency (%)
312
 
0.9%
479
 
6.1%
5242
18.7%
6378
29.2%
7302
23.4%
8166
12.8%
963
 
4.9%
1034
 
2.6%
1112
 
0.9%
125
 
0.4%
ValueCountFrequency (%)
125
 
0.4%
1112
 
0.9%
1034
 
2.6%
963
 
4.9%
8166
12.8%
7302
23.4%
6378
29.2%
5242
18.7%
479
 
6.1%
312
 
0.9%

Fireplaces
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
1
609 
0
589 
2
91 
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1293
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1609
47.1%
0589
45.6%
291
 
7.0%
34
 
0.3%

Length

2022-06-07T09:40:54.745731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:54.908731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1609
47.1%
0589
45.6%
291
 
7.0%
34
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1609
47.1%
0589
45.6%
291
 
7.0%
34
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1609
47.1%
0589
45.6%
291
 
7.0%
34
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1609
47.1%
0589
45.6%
291
 
7.0%
34
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1609
47.1%
0589
45.6%
291
 
7.0%
34
 
0.3%

GarageType
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Attchd
822 
Detchd
366 
BuiltIn
 
75
Basment
 
19
CarPort
 
7

Length

Max length7
Median length6
Mean length6.078112916
Min length6

Characters and Unicode

Total characters7859
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowDetchd
5th rowAttchd

Common Values

ValueCountFrequency (%)
Attchd822
63.6%
Detchd366
28.3%
BuiltIn75
 
5.8%
Basment19
 
1.5%
CarPort7
 
0.5%
2Types4
 
0.3%

Length

2022-06-07T09:40:55.058730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:55.233731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
attchd822
63.6%
detchd366
28.3%
builtin75
 
5.8%
basment19
 
1.5%
carport7
 
0.5%
2types4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
t2111
26.9%
c1188
15.1%
h1188
15.1%
d1188
15.1%
A822
 
10.5%
e389
 
4.9%
D366
 
4.7%
n94
 
1.2%
B94
 
1.2%
u75
 
1.0%
Other values (14)344
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6480
82.5%
Uppercase Letter1375
 
17.5%
Decimal Number4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t2111
32.6%
c1188
18.3%
h1188
18.3%
d1188
18.3%
e389
 
6.0%
n94
 
1.5%
u75
 
1.2%
i75
 
1.2%
l75
 
1.2%
a26
 
0.4%
Other values (6)71
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
A822
59.8%
D366
26.6%
B94
 
6.8%
I75
 
5.5%
C7
 
0.5%
P7
 
0.5%
T4
 
0.3%
Decimal Number
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7855
99.9%
Common4
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t2111
26.9%
c1188
15.1%
h1188
15.1%
d1188
15.1%
A822
 
10.5%
e389
 
5.0%
D366
 
4.7%
n94
 
1.2%
B94
 
1.2%
u75
 
1.0%
Other values (13)340
 
4.3%
Common
ValueCountFrequency (%)
24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7859
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t2111
26.9%
c1188
15.1%
h1188
15.1%
d1188
15.1%
A822
 
10.5%
e389
 
4.9%
D366
 
4.7%
n94
 
1.2%
B94
 
1.2%
u75
 
1.0%
Other values (14)344
 
4.4%

GarageFinish
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Unf
573 
RFn
404 
Fin
316 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3879
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRFn
2nd rowRFn
3rd rowRFn
4th rowUnf
5th rowRFn

Common Values

ValueCountFrequency (%)
Unf573
44.3%
RFn404
31.2%
Fin316
24.4%

Length

2022-06-07T09:40:55.401737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:55.659730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
unf573
44.3%
rfn404
31.2%
fin316
24.4%

Most occurring characters

ValueCountFrequency (%)
n1293
33.3%
F720
18.6%
U573
14.8%
f573
14.8%
R404
 
10.4%
i316
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2182
56.3%
Uppercase Letter1697
43.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1293
59.3%
f573
26.3%
i316
 
14.5%
Uppercase Letter
ValueCountFrequency (%)
F720
42.4%
U573
33.8%
R404
23.8%

Most occurring scripts

ValueCountFrequency (%)
Latin3879
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1293
33.3%
F720
18.6%
U573
14.8%
f573
14.8%
R404
 
10.4%
i316
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1293
33.3%
F720
18.6%
U573
14.8%
f573
14.8%
R404
 
10.4%
i316
 
8.1%

GarageCars
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
2
778 
1
361 
3
150 
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1293
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2778
60.2%
1361
27.9%
3150
 
11.6%
44
 
0.3%

Length

2022-06-07T09:40:55.809729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:55.967735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2778
60.2%
1361
27.9%
3150
 
11.6%
44
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2778
60.2%
1361
27.9%
3150
 
11.6%
44
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2778
60.2%
1361
27.9%
3150
 
11.6%
44
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2778
60.2%
1361
27.9%
3150
 
11.6%
44
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2778
60.2%
1361
27.9%
3150
 
11.6%
44
 
0.3%

GarageArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct410
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean492.2869296
Minimum160
Maximum1390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:56.140733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum160
5-th percentile240
Q1364
median482
Q3576
95-th percentile840
Maximum1390
Range1230
Interquartile range (IQR)212

Descriptive statistics

Standard deviation178.0449567
Coefficient of variation (CV)0.3616690715
Kurtosis0.7535682261
Mean492.2869296
Median Absolute Deviation (MAD)100
Skewness0.6921103339
Sum636527
Variance31700.00662
MonotonicityNot monotonic
2022-06-07T09:40:56.333729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44048
 
3.7%
57646
 
3.6%
24037
 
2.9%
48433
 
2.6%
52830
 
2.3%
28827
 
2.1%
26424
 
1.9%
48023
 
1.8%
40020
 
1.5%
30818
 
1.4%
Other values (400)987
76.3%
ValueCountFrequency (%)
1602
 
0.2%
1641
 
0.1%
1809
0.7%
1861
 
0.1%
1891
 
0.1%
1921
 
0.1%
1981
 
0.1%
2004
0.3%
2053
 
0.2%
2081
 
0.1%
ValueCountFrequency (%)
13901
0.1%
12481
0.1%
12201
0.1%
10691
0.1%
10531
0.1%
10521
0.1%
10431
0.1%
10251
0.1%
10141
0.1%
9831
0.1%

GarageQual
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
TA
1227 
Fa
 
47
Gd
 
14
Po
 
3
Ex
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2586
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA1227
94.9%
Fa47
 
3.6%
Gd14
 
1.1%
Po3
 
0.2%
Ex2
 
0.2%

Length

2022-06-07T09:40:56.532729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:56.698735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ta1227
94.9%
fa47
 
3.6%
gd14
 
1.1%
po3
 
0.2%
ex2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T1227
47.4%
A1227
47.4%
F47
 
1.8%
a47
 
1.8%
G14
 
0.5%
d14
 
0.5%
P3
 
0.1%
o3
 
0.1%
E2
 
0.1%
x2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2520
97.4%
Lowercase Letter66
 
2.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T1227
48.7%
A1227
48.7%
F47
 
1.9%
G14
 
0.6%
P3
 
0.1%
E2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a47
71.2%
d14
 
21.2%
o3
 
4.5%
x2
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2586
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T1227
47.4%
A1227
47.4%
F47
 
1.8%
a47
 
1.8%
G14
 
0.5%
d14
 
0.5%
P3
 
0.1%
o3
 
0.1%
E2
 
0.1%
x2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T1227
47.4%
A1227
47.4%
F47
 
1.8%
a47
 
1.8%
G14
 
0.5%
d14
 
0.5%
P3
 
0.1%
o3
 
0.1%
E2
 
0.1%
x2
 
0.1%

GarageCond
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
TA
1242 
Fa
 
33
Gd
 
9
Po
 
7
Ex
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2586
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA1242
96.1%
Fa33
 
2.6%
Gd9
 
0.7%
Po7
 
0.5%
Ex2
 
0.2%

Length

2022-06-07T09:40:56.835735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:57.000735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ta1242
96.1%
fa33
 
2.6%
gd9
 
0.7%
po7
 
0.5%
ex2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T1242
48.0%
A1242
48.0%
F33
 
1.3%
a33
 
1.3%
G9
 
0.3%
d9
 
0.3%
P7
 
0.3%
o7
 
0.3%
E2
 
0.1%
x2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2535
98.0%
Lowercase Letter51
 
2.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T1242
49.0%
A1242
49.0%
F33
 
1.3%
G9
 
0.4%
P7
 
0.3%
E2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a33
64.7%
d9
 
17.6%
o7
 
13.7%
x2
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Latin2586
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T1242
48.0%
A1242
48.0%
F33
 
1.3%
a33
 
1.3%
G9
 
0.3%
d9
 
0.3%
P7
 
0.3%
o7
 
0.3%
E2
 
0.1%
x2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T1242
48.0%
A1242
48.0%
F33
 
1.3%
a33
 
1.3%
G9
 
0.3%
d9
 
0.3%
P7
 
0.3%
o7
 
0.3%
E2
 
0.1%
x2
 
0.1%

PavedDrive
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Y
1215 
N
 
52
P
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1293
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y1215
94.0%
N52
 
4.0%
P26
 
2.0%

Length

2022-06-07T09:40:57.642735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:57.789735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
y1215
94.0%
n52
 
4.0%
p26
 
2.0%

Most occurring characters

ValueCountFrequency (%)
Y1215
94.0%
N52
 
4.0%
P26
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1293
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y1215
94.0%
N52
 
4.0%
P26
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1293
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y1215
94.0%
N52
 
4.0%
P26
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y1215
94.0%
N52
 
4.0%
P26
 
2.0%

SalePrice
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct589
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179730.2266
Minimum35311
Maximum395192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2022-06-07T09:40:57.954694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum35311
5-th percentile100000
Q1134500
median165500
Q3214000
95-th percentile315000
Maximum395192
Range359881
Interquartile range (IQR)79500

Descriptive statistics

Standard deviation64521.36388
Coefficient of variation (CV)0.3589900547
Kurtosis0.7700885554
Mean179730.2266
Median Absolute Deviation (MAD)36000
Skewness0.9789485
Sum232391183
Variance4163006398
MonotonicityNot monotonic
2022-06-07T09:40:58.159692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14000020
 
1.5%
13500016
 
1.2%
15500014
 
1.1%
11000013
 
1.0%
14500013
 
1.0%
19000012
 
0.9%
18500010
 
0.8%
14300010
 
0.8%
12500010
 
0.8%
13900010
 
0.8%
Other values (579)1165
90.1%
ValueCountFrequency (%)
353111
0.1%
400001
0.1%
559931
0.1%
585001
0.1%
600002
0.2%
623831
0.1%
645001
0.1%
665001
0.1%
670001
0.1%
684001
0.1%
ValueCountFrequency (%)
3951921
0.1%
3950001
0.1%
3946171
0.1%
3944321
0.1%
3925001
0.1%
3920001
0.1%
3862501
0.1%
3850001
0.1%
3839701
0.1%
3810001
0.1%

size
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
medium
617 
large
549 
small
127 

Length

Max length6
Median length5
Mean length5.477184841
Min length5

Characters and Unicode

Total characters7082
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmedium
2nd rowmedium
3rd rowlarge
4th rowmedium
5th rowlarge

Common Values

ValueCountFrequency (%)
medium617
47.7%
large549
42.5%
small127
 
9.8%

Length

2022-06-07T09:40:58.346693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T09:40:58.503690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
medium617
47.7%
large549
42.5%
small127
 
9.8%

Most occurring characters

ValueCountFrequency (%)
m1361
19.2%
e1166
16.5%
l803
11.3%
a676
9.5%
d617
8.7%
i617
8.7%
u617
8.7%
r549
7.8%
g549
7.8%
s127
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7082
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m1361
19.2%
e1166
16.5%
l803
11.3%
a676
9.5%
d617
8.7%
i617
8.7%
u617
8.7%
r549
7.8%
g549
7.8%
s127
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin7082
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m1361
19.2%
e1166
16.5%
l803
11.3%
a676
9.5%
d617
8.7%
i617
8.7%
u617
8.7%
r549
7.8%
g549
7.8%
s127
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII7082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m1361
19.2%
e1166
16.5%
l803
11.3%
a676
9.5%
d617
8.7%
i617
8.7%
u617
8.7%
r549
7.8%
g549
7.8%
s127
 
1.8%

Interactions

2022-06-07T09:40:34.674347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:44.565320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:47.621448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:50.974287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:53.851056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:56.652598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:59.905600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:02.897598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:06.110595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:09.359594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:12.433599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:15.604591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:18.738403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:21.989130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:25.172130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:28.540129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:31.594351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:34.850352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:44.788318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:47.798452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:51.153289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:54.018056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:56.978601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:00.126599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:03.083599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:06.292594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:09.536593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:12.596601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:15.786593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:18.915898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:22.196131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:25.386131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:28.712127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:31.776348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:35.027346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:44.970322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:47.972454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:51.328055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:54.184056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:57.159599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:00.318601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:03.303597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:06.472594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:09.716595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:12.762596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:15.974594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:19.089133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:22.404129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:25.591132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:28.883130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:31.959347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:35.203347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:45.149323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:48.228451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:51.496056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:54.346058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:57.334602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:00.496598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:03.489600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:06.635593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:09.883594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:12.921595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:16.147595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:19.264127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:22.575129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:25.766129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:29.057132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:32.145347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:35.368671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:45.325320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:48.428454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:51.661058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:54.508060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:39:57.494602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:00.661602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:03.662598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:06.801595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:10.042593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:13.088594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:16.335591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:19.450128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:22.784128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:25.937128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-07T09:40:18.565400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:21.755127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:24.904128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:28.350126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:31.396355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T09:40:34.491348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-06-07T09:40:58.802693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-07T09:40:59.256697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-07T09:40:59.633748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-07T09:41:00.155752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-07T09:41:00.625751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-07T09:40:37.935112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-07T09:40:40.117106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexIdMSSubClassMSZoningLotFrontageLotAreaLotShapeLandContourLotConfigNeighborhoodBldgTypeHouseStyleOverallQualOverallCondYearBuiltRoofStyleExterior1stMasVnrTypeExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtUnfSFTotalBsmtSFHeatingQC1stFlrSFGrLivAreaBsmtFullBathFullBathHalfBathBedroomAbvGrKitchenQualTotRmsAbvGrdFireplacesGarageTypeGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveSalePricesize
00160RL65.08450RegLvlInsideCollgCr1Fam2Story752003GableVinylSdBrkFaceGdTAPConcGdTANoGLQ706Unf150856Ex85617101213Gd80AttchdRFn2548TATAY208500medium
11220RL80.09600RegLvlFR2Veenker1Fam1Story681976GableMetalSdNoneTATACBlockGdTAGdALQ978Unf2841262Ex126212620203TA61AttchdRFn2460TATAY181500medium
22360RL68.011250IR1LvlInsideCollgCr1Fam2Story752001GableVinylSdBrkFaceGdTAPConcGdTAMnGLQ486Unf434920Ex92017861213Gd61AttchdRFn2608TATAY223500large
33470RL60.09550IR1LvlCornerCrawfor1Fam2Story751915GableWd SdngNoneTATABrkTilTAGdNoALQ216Unf540756Gd96117171103Gd71DetchdUnf3642TATAY140000medium
44560RL84.014260IR1LvlFR2NoRidge1Fam2Story852000GableVinylSdBrkFaceGdTAPConcGdTAAvGLQ655Unf4901145Ex114521981214Gd91AttchdRFn3836TATAY250000large
55650RL85.014115IR1LvlInsideMitchel1Fam1.5Fin551993GableVinylSdNoneTATAWoodGdTANoGLQ732Unf64796Ex79613621111TA50AttchdUnf2480TATAY143000large
66720RL75.010084RegLvlInsideSomerst1Fam1Story852004GableVinylSdStoneGdTAPConcExTAAvGLQ1369Unf3171686Ex169416941203Gd71AttchdRFn2636TATAY307000large
77860RL69.010382IR1LvlCornerNWAmes1Fam2Story761973GableHdBoardStoneTATACBlockGdTAMnALQ859BLQ2161107Ex110720901213TA72AttchdRFn2484TATAY200000large
88950RM51.06120RegLvlInsideOldTown1Fam1.5Fin751931GableBrkFaceNoneTATABrkTilTATANoUnf0Unf952952Gd102217740202TA82DetchdUnf2468FaTAY129900medium
9910190RL50.07420RegLvlCornerBrkSide2fmCon1.5Unf561939GableMetalSdNoneTATABrkTilTATANoGLQ851Unf140991Ex107710771102TA52AttchdRFn1205GdTAY118000medium

Last rows

df_indexIdMSSubClassMSZoningLotFrontageLotAreaLotShapeLandContourLotConfigNeighborhoodBldgTypeHouseStyleOverallQualOverallCondYearBuiltRoofStyleExterior1stMasVnrTypeExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtUnfSFTotalBsmtSFHeatingQC1stFlrSFGrLivAreaBsmtFullBathFullBathHalfBathBedroomAbvGrKitchenQualTotRmsAbvGrdFireplacesGarageTypeGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveSalePricesize
12831447144860RL80.010000RegLvlInsideCollgCr1Fam2Story851995GableVinylSdBrkFaceGdTAPConcGdTANoGLQ1079Unf1411220Ex122020901213Gd81AttchdRFn2556TATAY240000large
12841448144950RL70.011767RegLvlInsideEdwards1Fam2Story471910GableMetalSdNoneTATACBlockFaTANoUnf0Unf560560Gd79613460112TA60DetchdUnf1384FaTAY112000large
12851451145220RL78.09262RegLvlInsideSomerst1Fam1Story852008GableCemntBdStoneGdTAPConcGdTANoUnf0Unf15731573Ex157815780203Ex71AttchdFin3840TATAY287090medium
128614521453180RM35.03675RegLvlInsideEdwardsTwnhsESLvl552005GableVinylSdBrkFaceTATAPConcGdTAGdGLQ547Unf0547Gd107210721102TA50BasmentFin2525TATAY145000small
12871454145520FV62.07500RegLvlInsideSomerst1Fam1Story752004GableVinylSdNoneGdTAPConcGdTANoGLQ410Unf8111221Ex122112211202Gd60AttchdRFn2400TATAY185000medium
12881455145660RL62.07917RegLvlInsideGilbert1Fam2Story651999GableVinylSdNoneTATAPConcGdTANoUnf0Unf953953Ex95316470213TA71AttchdRFn2460TATAY175000medium
12891456145720RL85.013175RegLvlInsideNWAmes1Fam1Story661978GablePlywoodStoneTATACBlockGdTANoALQ790Rec5891542TA207320731203TA72AttchdUnf2500TATAY210000large
12901457145870RL66.09042RegLvlInsideCrawfor1Fam2Story791941GableCemntBdNoneExGdStoneTAGdNoGLQ275Unf8771152Ex118823400204Gd92AttchdRFn1252TATAY266500medium
12911458145920RL68.09717RegLvlInsideNAmes1Fam1Story561950HipMetalSdNoneTATACBlockTATAMnGLQ49Rec01078Gd107810781102Gd50AttchdUnf1240TATAY142125medium
12921459146020RL75.09937RegLvlInsideEdwards1Fam1Story561965GableHdBoardNoneGdTACBlockTATANoBLQ830LwQ1361256Gd125612561113TA60AttchdFin1276TATAY147500medium